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补丁变压器网络:一种基于可穿戴传感器的摔倒检测方法.

Shaobing Wang1, Jiang Wu1

  • 1School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China.

Sensors (Basel, Switzerland)
|July 29, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了用于老年人穿戴式传感器落检测的补丁变压器网络 (PTN). 该PTN算法准确且快速检测下跌,这对于预防伤害至关重要.

关键词:
在美国,CNN是CNN.变压器编码器编码器深度学习是一种深度学习.落检测系统 落检测系统 落检测系统功能提取 特性提取

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科学领域:

  • 老年学是一门学科.
  • 生物医学工程 生物医学工程
  • 人工智能的人工智能

背景情况:

  • 布对老年人的健康构成重大风险.
  • 及时发现落对于预防二次伤害至关重要.

研究的目的:

  • 提出一种基于可穿戴传感器的新型摔倒检测算法.
  • 为了提高老年人跌倒检测的准确性和速度.

主要方法:

  • 开发了一种包含卷积,变压器编码和线性分类层的补丁变压器网络 (PTN).
  • 利用多头自我注意力进行全球特征学习,并使用全球平均聚合 (GAP) 来进行特征类别相关性.
  • 在SisFall和UnMib SHAR公共数据集上测试了算法.

主要成果:

  • 在SisFall上达到99.86%的高准确率,在UnMib SHAR上达到99.14%.
  • 演示的快速检测时间分别为0.004s和0.001s.
  • PTN 模型具有较少的参数和较低的计算复杂性.

结论:

  • 拟议的补丁变压器网络 (PTN) 为基于可穿戴传感器的摔倒检测提供了一个及时而准确的解决方案.
  • 这种方法对于提高老年人群的安全和福祉至关重要.